A MILP Algorithm for the Minimization of Train Delay and Energy Consumption. In: (a cura di): Sforza A., Sterle C., Optimization and Decision Science: Methodologies and Applications. ODS 2017. SPRINGER PROCEEDINGS IN MATHEMATICS & STATISTICS, vol. 217, p. 485-493, Springer, ISSN: 2194-1009, Sorrento, Italy, 4-7 September 2017, doi: https://doi.org/10.1007/978-3-319-67308-0_49

A new timetable must be calculated in real-time when train operations are perturbed. The energy consumption is becoming a central issue both from the environmental and economic perspective but it is usually neglected in the timetable recalculation. In this paper, we formalize the real-time Energy Consumption Minimization Problem (rtECMP). The rtECMP is the real-time optimization problem of finding the driving regime combination for each train that minimizes the energy consumption, respecting given routing and precedences between trains. We model the trade-off between minimizing the energy consumption and the total delay by considering as objective their weighted sum. We propose an algorithm to solve the rtECMP, based on the solution of a mixed-integer linear programming (MILP) model. We test this algorithm on the Pierrefitte-Gonesse control area, which is a critical area in France with dense mixed traffic. In particular, we consider a one-hour traffic perturbation. In this situation, we take into account different routing and precedence possibilities and we solve the corresponding rtECMP. This experimental analysis shows the influence on the solution of the weights associated with energy consumption and delay in the objective function. The results show that the problem is too difficult to be solved to optimality in real time, but is indeed tractable